Is the sense of touch a mechanism for human babies' learning of visual concepts? If so, can we quantify its importance, and to what extent do babies rely on their sense of touch for visual learning? To approach these questions in a principled way, we propose a structured coding system for baby-centric touch events, yielding a dataset of 264k two-second clips of touch events coded according to this system. Using this dataset, we pretrain developmentally grounded models that reveal promising insights into the nature of baby learning from touch.
How does a system that merely predicts the world come to distinguish its own causal influence from everything else? We trace this transition in a minimal 192-dimensional GRU through a developmental sequence -- 6 experimental stages, 12 falsified alternatives, and cross-signal validation. Starting with no action or self-representation, we add components one at a time, tracking whether the system distinguishes self-caused from world-caused changes. The central finding is the encoding gap: a system can perfectly compensate for its own actions in prediction while failing to encode "I am acting" as a readable state -- implicit causal use and explicit self-representation are dissociated capabilities. The developmental path crosses this gap when four conditions are jointly satisfied: (1) persistent state that forms stable attractors, (2) a causal action loop linking the system's output to its input, (3) proprioceptive feedback that makes implicit causal knowledge explicit, and (4) asynchronous awakening -- consolidating perceptual learning before action learning, which yields the only configuration robust to hyperparameter choice. We propose agency gain (A = Err_world - Err_self), the predictive advantage of knowing one's own action, as a continuous metric that generalizes across signal types. A decisive test confirms the causal grounding of the encoding: after the external training signal is removed, the causal agent retains its self-representation at 94.9% while a statistically-matched control collapses to 53.9%. Self-representation persists only when causally useful for prediction -- an intrinsic property of the causal loop, not a training artifact.